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Multi-circular synthetic aperture radar (MCSAR) has the full 360° 3-D imaging ability by using multiple circular tracks at different elevation angles. Compressive sensing (CS) based imaging methods provide a solution for MCSAR elevation reconstruction when the circular tracks distribute sparsely and non-uniformly. When processing the MCSAR real data, the issues of off-grid effect and the spurious...
In recent years, compressed sensing (CS) has been applied in the field of synthetic aperture radar (SAR) imaging and shows great potential. The existing models are, however, based on application of the sensing matrix acquired by the exact observation functions. As a result, the corresponding reconstruction algorithms are much more time consuming than traditional matched filter (MF)-based focusing...
Long-term wireless neural recording systems which are subject to stringent power consumption, are highly desired to reduce the rate of data transmission and computation complexity. In this paper, we propose using a combination of on-chip neural action potentials (‘spikes’) detection system and compressive sensing (CS) techniques to reduce the power required for data transmission and a random Bernoulli...
Range ambiguity in synthetic aperture radar (SAR) imaging primarily arises from scattered energy of bright targets outside the interested region. So to reduce the ambiguity, we need to identify these targets additionally, which yields an ill-posed problem. To find a feasible solution where the range ambiguity can be sufficiently reduced, we propose in this paper a new method using compressed sensing,...
Compressed sensing (CS) is a new theory that enables sampling below the Nyquist rate, while the quality of reconstruction is guaranteed. This theory is applied to sparse circumstance. Based on this theory, this paper proposes a feasible method for the spaceborne stripmap synthetic aperture radar (SAR) raw data imaging. The technique presented is introduced as an alternative option to the traditional...
Imaging algorithm based on compressed sensing (CS) is studied for multiple inputs and multiple outputs (MIMO) SAR using orthogonal code waveforms. It can reduce the sampling rate and improve reconstruction results compared with traditional SAR algorithms. Through modelling all the echoes of orthogonal signals, it can also reduce the range ambiguity caused be nonideal orthogonality of multiple transmitted...
In this paper, a novel synthetic aperture radar (SAR) imaging method based on L1/2 regularization is proposed. Our method implements SAR imaging from compressed measurements with high resolution, enhanced features, reduced sidelobes and suppressed artifacts. Real SAR data experiments are implemented to demonstrate the outperformance of our method. The experiment results demonstrate that our method...
The displaced phase center antenna (DPCA) synthetic aperture radar (SAR) has the potential to achieve high azimuth resolution and wide swath. Its pulse repletion frequency (PRF) has to be selected such that SAR platform moves just one half of its total antenna length between subsequent radar pulses. If this condition is not satisfied, there will be nonuniform sampling in azimuth and azimuth ambiguities...
Recent theory of compressed sensing (CS) suggested that exact recovery of an unknown sparse signal can be achieved from few measurements with overwhelming probability. In this paper, we combine CS technology with a random noise SAR and proposed the concept of random noise SAR based on CS. The block diagram of the radar system and the collected data processing procedure was presented. Theoretic analysis...
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